Before you start

Set my seed

# Any number can be chose
set.seed(567890)

Goals for this file

Use raw fastq and generate the quality plots to asses the quality of reads
Filter and trim out bad sequences and bases from our sequencing files
Write out fastq files with high quality sequences
Evaluate the quality from our filter and trim.
Infer errors on forward and reverse reads individually
Identified ASVs on forward and reverse reads separately using the error model.
Merge forward and reverse ASVs into “contigous ASVs”.
Generate ASV count table. (otu_table input for phyloseq.).

Output that we need:

ASV count table: otu_table
Taxonomy table tax_table
Sample information: sample_table track the reads lost throughout DADA2 workflow.

Load Libraries

#Effecient package loading with pacman
pacman::p_load(tidyverse, devtools, dada2, phyloseq, patchwork, DT,
               install = FALSE)

Load Data

#Set the raw fastq path to the raw sequencing files
#Path to the fastq files
raw_fastqs_path <- "data/01_DADA2/00_trimmed_fastq"
raw_fastqs_path

## [1] "data/01_DADA2/00_trimmed_fastq"

#What files are in this path (Intuition check)
list.files(raw_fastqs_path)

##  [1] "SRR17060816_trim_1.fq.gz" "SRR17060816_trim_2.fq.gz"
##  [3] "SRR17060817_trim_1.fq.gz" "SRR17060817_trim_2.fq.gz"
##  [5] "SRR17060818_trim_1.fq.gz" "SRR17060818_trim_2.fq.gz"
##  [7] "SRR17060819_trim_1.fq.gz" "SRR17060819_trim_2.fq.gz"
##  [9] "SRR17060820_trim_1.fq.gz" "SRR17060820_trim_2.fq.gz"
## [11] "SRR17060821_trim_1.fq.gz" "SRR17060821_trim_2.fq.gz"
## [13] "SRR17060822_trim_1.fq.gz" "SRR17060822_trim_2.fq.gz"
## [15] "SRR17060823_trim_1.fq.gz" "SRR17060823_trim_2.fq.gz"
## [17] "SRR17060824_trim_1.fq.gz" "SRR17060824_trim_2.fq.gz"
## [19] "SRR17060825_trim_1.fq.gz" "SRR17060825_trim_2.fq.gz"
## [21] "SRR17060826_trim_1.fq.gz" "SRR17060826_trim_2.fq.gz"
## [23] "SRR17060827_trim_1.fq.gz" "SRR17060827_trim_2.fq.gz"
## [25] "SRR17060828_trim_1.fq.gz" "SRR17060828_trim_2.fq.gz"
## [27] "SRR17060829_trim_1.fq.gz" "SRR17060829_trim_2.fq.gz"
## [29] "SRR17060830_trim_1.fq.gz" "SRR17060830_trim_2.fq.gz"
## [31] "SRR17060831_trim_1.fq.gz" "SRR17060831_trim_2.fq.gz"
## [33] "SRR17060832_trim_1.fq.gz" "SRR17060832_trim_2.fq.gz"
## [35] "SRR17060833_trim_1.fq.gz" "SRR17060833_trim_2.fq.gz"
## [37] "SRR17060834_trim_1.fq.gz" "SRR17060834_trim_2.fq.gz"
## [39] "SRR17060835_trim_1.fq.gz" "SRR17060835_trim_2.fq.gz"
## [41] "SRR17060836_trim_1.fq.gz" "SRR17060836_trim_2.fq.gz"
## [43] "SRR17060837_trim_1.fq.gz" "SRR17060837_trim_2.fq.gz"
## [45] "SRR17060838_trim_1.fq.gz" "SRR17060838_trim_2.fq.gz"
## [47] "SRR17060839_trim_1.fq.gz" "SRR17060839_trim_2.fq.gz"
## [49] "SRR17060840_trim_1.fq.gz" "SRR17060840_trim_2.fq.gz"
## [51] "SRR17060841_trim_1.fq.gz" "SRR17060841_trim_2.fq.gz"
## [53] "SRR17060842_trim_1.fq.gz" "SRR17060842_trim_2.fq.gz"
## [55] "SRR17060843_trim_1.fq.gz" "SRR17060843_trim_2.fq.gz"
## [57] "SRR17060844_trim_1.fq.gz" "SRR17060844_trim_2.fq.gz"
## [59] "SRR17060845_trim_1.fq.gz" "SRR17060845_trim_2.fq.gz"
## [61] "SRR17060846_trim_1.fq.gz" "SRR17060846_trim_2.fq.gz"
## [63] "SRR17060847_trim_1.fq.gz" "SRR17060847_trim_2.fq.gz"

#How many files are there?
str(list.files(raw_fastqs_path))

##  chr [1:64] "SRR17060816_trim_1.fq.gz" "SRR17060816_trim_2.fq.gz" ...

#Create a vector of forward reads
forward_reads <- list.files(raw_fastqs_path, pattern = "_trim_1.fq.gz", full.names = TRUE) 
#Intuition check
head(forward_reads)

## [1] "data/01_DADA2/00_trimmed_fastq/SRR17060816_trim_1.fq.gz"
## [2] "data/01_DADA2/00_trimmed_fastq/SRR17060817_trim_1.fq.gz"
## [3] "data/01_DADA2/00_trimmed_fastq/SRR17060818_trim_1.fq.gz"
## [4] "data/01_DADA2/00_trimmed_fastq/SRR17060819_trim_1.fq.gz"
## [5] "data/01_DADA2/00_trimmed_fastq/SRR17060820_trim_1.fq.gz"
## [6] "data/01_DADA2/00_trimmed_fastq/SRR17060821_trim_1.fq.gz"

#Create a vector of reverse reads
reverse_reads <-list.files(raw_fastqs_path, pattern = "_trim_2.fq.gz", full.names = TRUE)
#Intuition check
head(reverse_reads)

## [1] "data/01_DADA2/00_trimmed_fastq/SRR17060816_trim_2.fq.gz"
## [2] "data/01_DADA2/00_trimmed_fastq/SRR17060817_trim_2.fq.gz"
## [3] "data/01_DADA2/00_trimmed_fastq/SRR17060818_trim_2.fq.gz"
## [4] "data/01_DADA2/00_trimmed_fastq/SRR17060819_trim_2.fq.gz"
## [5] "data/01_DADA2/00_trimmed_fastq/SRR17060820_trim_2.fq.gz"
## [6] "data/01_DADA2/00_trimmed_fastq/SRR17060821_trim_2.fq.gz"

Raw Quality plots

# Randomly select 12 samples from dataset to evaluate 
# Selecting 12 is typically better than 2 (like we did in class for efficiency)
random_samples <- sample(1:length(reverse_reads), size = 12)
random_samples

##  [1] 16 22 15  1 14  6 30 27 11 13 23 32

# Calculate and plot quality of these two samples
forward_filteredQual_plot_12 <- plotQualityProfile(forward_reads[random_samples]) + 
  labs(title = "Forward Read: Raw Quality")

reverse_filteredQual_plot_12 <- plotQualityProfile(reverse_reads[random_samples]) + 
  labs(title = "Reverse Read: Raw Quality")

# Plot them together with patchwork
forward_filteredQual_plot_12 + reverse_filteredQual_plot_12

Aggregated Raw Quality Plots

# Aggregate all QC plots 
# Forward reads
forward_preQC_plot <- 
  plotQualityProfile(forward_reads, aggregate = TRUE) + 
  labs(title = "Forward Pre-QC")

# reverse reads
reverse_preQC_plot <- 
  plotQualityProfile(reverse_reads, aggregate = TRUE) + 
  labs(title = "Reverse Pre-QC")

preQC_aggregate_plot <- 
  # Plot the forward and reverse together 
  forward_preQC_plot + reverse_preQC_plot

# Show the plot
preQC_aggregate_plot

Prepare a placeholder for filtered reads

# vector of our samples, extract the sample information from our file
samples <- sapply(strsplit(basename(forward_reads), "_"), `[`,1)
#Intuition check
head(samples)

## [1] "SRR17060816" "SRR17060817" "SRR17060818" "SRR17060819" "SRR17060820"
## [6] "SRR17060821"

#place filtered reads into filtered_fastqs_path
filtered_fastqs_path <- "data/01_DADA2/02_filtered_fastqs"
filtered_fastqs_path

## [1] "data/01_DADA2/02_filtered_fastqs"

# create 2 variables : filtered_F, filtered_R
filtered_forward_reads <- 
  file.path(filtered_fastqs_path, paste0(samples, "_R1_filtered.fastq.gz"))

#Intuition check
head(filtered_forward_reads)

## [1] "data/01_DADA2/02_filtered_fastqs/SRR17060816_R1_filtered.fastq.gz"
## [2] "data/01_DADA2/02_filtered_fastqs/SRR17060817_R1_filtered.fastq.gz"
## [3] "data/01_DADA2/02_filtered_fastqs/SRR17060818_R1_filtered.fastq.gz"
## [4] "data/01_DADA2/02_filtered_fastqs/SRR17060819_R1_filtered.fastq.gz"
## [5] "data/01_DADA2/02_filtered_fastqs/SRR17060820_R1_filtered.fastq.gz"
## [6] "data/01_DADA2/02_filtered_fastqs/SRR17060821_R1_filtered.fastq.gz"

length(filtered_forward_reads)

## [1] 32

filtered_reverse_reads <- file.path(filtered_fastqs_path, paste0(samples,
                                                  "_R2_filtered.fastq.gz"))
#Intuition check
length(filtered_reverse_reads)

## [1] 32

Filter and Trim Reads

Parameters of filter and trim DEPEND ON THE DATASET

maxN = number of N bases. Remove all Ns from the data.
maxEE = quality filtering threshold applied to expected errors. By default, all expected errors. Mar recommends using c(1,1). Here, if there is maxEE expected errors, its okay. If more, throw away sequence.
trimLeft = trim certain number of base pairs on start of each read
truncQ = truncate reads at the first instance of a quality score less than or equal to selected number. Chose 2
rm.phix = remove phi x
compress = make filtered files .gzipped
multithread = multithread

#Assign a vector to filtered reads
#Trim out poor bases
#Write out filtered fastq files
filtered_reads <-
  filterAndTrim(fwd = forward_reads, filt = filtered_forward_reads,
              rev = reverse_reads, filt.rev = filtered_reverse_reads,
               trimLeft = c(15,9),truncLen = c(245,230),
              maxN = 0, maxEE = c(2, 2),truncQ = 2, rm.phix = TRUE,
              compress = TRUE, multithread = 6)

Trimmed Quality Plots

# Plot the 12 random samples after QC
forward_filteredQual_plot_12 <- 
  plotQualityProfile(filtered_forward_reads[random_samples]) + 
  labs(title = "Trimmed Forward Read Quality")

reverse_filteredQual_plot_12 <- 
  plotQualityProfile(filtered_reverse_reads[random_samples]) + 
  labs(title = "Trimmed Reverse Read Quality")

# Put the two plots together 
forward_filteredQual_plot_12 + reverse_filteredQual_plot_12

Aggregated Trimmed Plots

# Aggregate all QC plots 
# Forward reads
forward_postQC_plot <- 
  plotQualityProfile(filtered_forward_reads, aggregate = TRUE) + 
  labs(title = "Forward Post-QC")

# reverse reads
reverse_postQC_plot <- 
  plotQualityProfile(filtered_reverse_reads, aggregate = TRUE) + 
  labs(title = "Reverse Post-QC")

postQC_aggregate_plot <- 
  # Plot the forward and reverse together 
  forward_postQC_plot + reverse_postQC_plot

# Show the plot
postQC_aggregate_plot

Stats on read output from `filterAndTrim`

#Make output into dataframe
filtered_df <- as.data.frame(filtered_reads)
head(filtered_df)

##                          reads.in reads.out
## SRR17060816_trim_1.fq.gz   285558       549
## SRR17060817_trim_1.fq.gz   676817       278
## SRR17060818_trim_1.fq.gz   591364       423
## SRR17060819_trim_1.fq.gz   379452       714
## SRR17060820_trim_1.fq.gz   570270       604
## SRR17060821_trim_1.fq.gz   556682       555

# calculate some stats
filtered_df %>%
  reframe(median_reads_in = median(reads.in),
          median_reads_out = median(reads.out),
          median_percent_retained = (median(reads.out)/median(reads.in)))

##   median_reads_in median_reads_out median_percent_retained
## 1        294748.5            308.5             0.001046655

[Insert paragraph interpreting the results above]

How many reads got through? Is it “enough”?
Should you play with the parameters in filterAndTrim() more? If so, which parameters?

Error Modeling

Note every sequencing run needs to be run separately! The error model MUST be run separately on each illumina dataset. If you’d like to combine the datasets from multiple sequencing runs, you’ll need to do the exact same filterAndTrim() step AND, very importantly, you’ll need to have the same primer and ASV length expected by the output.

Infer error rates for all possible transitions within purines and pyrimidines (A<>G or C<>T) and transversions between all purine and pyrimidine combinations.

Error model is learned by alternating estimation of the error rates and inference of sample composition until they converge.

Starts with the assumption that the error rates are the maximum (takes the most abundant sequence (“center”) and assumes it’s the only sequence not caused by errors).
Compares the other sequences to the most abundant sequence.
Uses at most 10⁸ nucleotides for the error estimation.
Uses parametric error estimation function of loess fit on the observed error rates.

#Forward reads
error_forward_reads <-
  learnErrors(filtered_forward_reads, multithread = TRUE)

## 3346040 total bases in 14548 reads from 32 samples will be used for learning the error rates.

#Plot forward reads errors
forward_error_plot <-
  plotErrors(error_forward_reads, nominalQ = TRUE) + 
  labs(title =     "Forward Read Error Model")

#Reverse reads
error_reverse_reads <-
  learnErrors(filtered_reverse_reads, multithread = TRUE)

## 3215108 total bases in 14548 reads from 32 samples will be used for learning the error rates.

#Plot reverse reads errors
reverse_error_plot <-
  plotErrors(error_reverse_reads, nominalQ = TRUE) +
    labs(title = "Reverse Read Error Model")

#Put the two plots together
forward_error_plot + reverse_error_plot

## Warning in scale_y_log10(): log-10 transformation introduced infinite values.
## log-10 transformation introduced infinite values.
## log-10 transformation introduced infinite values.

[Insert paragraph interpreting the plot above above]

The error rates for each possible transition (A→C, A→G, …) are shown in the plot above.

Details of the plot: - Points: The observed error rates for each consensus quality score.
- Black line: Estimated error rates after convergence of the machine-learning algorithm.
- Red line: The error rates expected under the nominal definition of the Q-score.

Similar to what is mentioned in the dada2 tutorial: the estimated error rates (black line) are a “reasonably good” fit to the observed rates (points), and the error rates drop with increased quality as expected. We can now infer ASVs!

Infer ASVs

An important note: This process occurs separately on forward and reverse reads! This is quite a different approach from how OTUs are identified in Mothur and also from UCHIME, oligotyping, and other OTU, MED, and ASV approaches.

#Infer forward ASVs
dada_forward <- dada(filtered_forward_reads, 
                     err = error_forward_reads,
                     multithread = 6)

## Sample 1 - 549 reads in 543 unique sequences.
## Sample 2 - 278 reads in 203 unique sequences.
## Sample 3 - 423 reads in 361 unique sequences.
## Sample 4 - 714 reads in 623 unique sequences.
## Sample 5 - 604 reads in 472 unique sequences.
## Sample 6 - 555 reads in 409 unique sequences.
## Sample 7 - 129 reads in 128 unique sequences.
## Sample 8 - 246 reads in 244 unique sequences.
## Sample 9 - 339 reads in 297 unique sequences.
## Sample 10 - 103 reads in 103 unique sequences.
## Sample 11 - 126 reads in 126 unique sequences.
## Sample 12 - 94 reads in 93 unique sequences.
## Sample 13 - 106 reads in 106 unique sequences.
## Sample 14 - 4 reads in 4 unique sequences.
## Sample 15 - 164 reads in 97 unique sequences.
## Sample 16 - 1250 reads in 754 unique sequences.
## Sample 17 - 457 reads in 287 unique sequences.
## Sample 18 - 693 reads in 566 unique sequences.
## Sample 19 - 2237 reads in 1358 unique sequences.
## Sample 20 - 755 reads in 698 unique sequences.
## Sample 21 - 528 reads in 343 unique sequences.
## Sample 22 - 2002 reads in 966 unique sequences.
## Sample 23 - 634 reads in 433 unique sequences.
## Sample 24 - 5 reads in 5 unique sequences.
## Sample 25 - 208 reads in 208 unique sequences.
## Sample 26 - 179 reads in 176 unique sequences.
## Sample 27 - 157 reads in 154 unique sequences.
## Sample 28 - 118 reads in 116 unique sequences.
## Sample 29 - 155 reads in 151 unique sequences.
## Sample 30 - 6 reads in 6 unique sequences.
## Sample 31 - 385 reads in 366 unique sequences.
## Sample 32 - 345 reads in 285 unique sequences.

#Infer reverse ASVs
dada_reverse <- dada(filtered_reverse_reads, 
                     err = error_reverse_reads, 
                     multithread = 6)

## Sample 1 - 549 reads in 496 unique sequences.
## Sample 2 - 278 reads in 212 unique sequences.
## Sample 3 - 423 reads in 330 unique sequences.
## Sample 4 - 714 reads in 664 unique sequences.
## Sample 5 - 604 reads in 491 unique sequences.
## Sample 6 - 555 reads in 460 unique sequences.
## Sample 7 - 129 reads in 114 unique sequences.
## Sample 8 - 246 reads in 227 unique sequences.
## Sample 9 - 339 reads in 301 unique sequences.
## Sample 10 - 103 reads in 92 unique sequences.
## Sample 11 - 126 reads in 106 unique sequences.
## Sample 12 - 94 reads in 82 unique sequences.
## Sample 13 - 106 reads in 98 unique sequences.
## Sample 14 - 4 reads in 4 unique sequences.
## Sample 15 - 164 reads in 102 unique sequences.
## Sample 16 - 1250 reads in 873 unique sequences.
## Sample 17 - 457 reads in 327 unique sequences.
## Sample 18 - 693 reads in 546 unique sequences.
## Sample 19 - 2237 reads in 1827 unique sequences.
## Sample 20 - 755 reads in 688 unique sequences.
## Sample 21 - 528 reads in 366 unique sequences.
## Sample 22 - 2002 reads in 1586 unique sequences.
## Sample 23 - 634 reads in 467 unique sequences.
## Sample 24 - 5 reads in 5 unique sequences.
## Sample 25 - 208 reads in 166 unique sequences.
## Sample 26 - 179 reads in 155 unique sequences.
## Sample 27 - 157 reads in 122 unique sequences.
## Sample 28 - 118 reads in 93 unique sequences.
## Sample 29 - 155 reads in 145 unique sequences.
## Sample 30 - 6 reads in 6 unique sequences.
## Sample 31 - 385 reads in 360 unique sequences.
## Sample 32 - 345 reads in 294 unique sequences.

#Inspect
dada_forward[1]

## $SRR17060816_R1_filtered.fastq.gz
## dada-class: object describing DADA2 denoising results
## 6 sequence variants were inferred from 543 input unique sequences.
## Key parameters: OMEGA_A = 1e-40, OMEGA_C = 1e-40, BAND_SIZE = 16

dada_reverse[1]

## $SRR17060816_R2_filtered.fastq.gz
## dada-class: object describing DADA2 denoising results
## 12 sequence variants were inferred from 496 input unique sequences.
## Key parameters: OMEGA_A = 1e-40, OMEGA_C = 1e-40, BAND_SIZE = 16

dada_forward[12]

## $SRR17060827_R1_filtered.fastq.gz
## dada-class: object describing DADA2 denoising results
## 1 sequence variants were inferred from 93 input unique sequences.
## Key parameters: OMEGA_A = 1e-40, OMEGA_C = 1e-40, BAND_SIZE = 16

dada_reverse[12]

## $SRR17060827_R2_filtered.fastq.gz
## dada-class: object describing DADA2 denoising results
## 4 sequence variants were inferred from 82 input unique sequences.
## Key parameters: OMEGA_A = 1e-40, OMEGA_C = 1e-40, BAND_SIZE = 16

Merge Forward and Reverse ASVs

Now, merge the forward and reverse ASVs into contigs.

# merge forward and reverse ASVs
merged_ASVs <- mergePairs(dada_forward, filtered_forward_reads, 
                          dada_reverse, filtered_reverse_reads,
                          verbose = TRUE)

## 192 paired-reads (in 5 unique pairings) successfully merged out of 274 (in 8 pairings) input.

## 155 paired-reads (in 5 unique pairings) successfully merged out of 159 (in 7 pairings) input.

## 270 paired-reads (in 3 unique pairings) successfully merged out of 295 (in 5 pairings) input.

## 283 paired-reads (in 15 unique pairings) successfully merged out of 393 (in 23 pairings) input.

## 157 paired-reads (in 5 unique pairings) successfully merged out of 299 (in 21 pairings) input.

## 223 paired-reads (in 10 unique pairings) successfully merged out of 414 (in 22 pairings) input.

## No paired-reads (in ZERO unique pairings) successfully merged out of 129 pairings) input.

## 44 paired-reads (in 1 unique pairings) successfully merged out of 44 (in 1 pairings) input.

## 112 paired-reads (in 5 unique pairings) successfully merged out of 137 (in 8 pairings) input.

## No paired-reads (in ZERO unique pairings) successfully merged out of 103 pairings) input.

## 0 paired-reads (in 0 unique pairings) successfully merged out of 3 (in 1 pairings) input.

## 19 paired-reads (in 1 unique pairings) successfully merged out of 21 (in 2 pairings) input.

## No paired-reads (in ZERO unique pairings) successfully merged out of 106 pairings) input.

## No paired-reads (in ZERO unique pairings) successfully merged out of 4 pairings) input.

## 109 paired-reads (in 2 unique pairings) successfully merged out of 109 (in 2 pairings) input.

## 779 paired-reads (in 20 unique pairings) successfully merged out of 977 (in 37 pairings) input.

## 179 paired-reads (in 3 unique pairings) successfully merged out of 269 (in 13 pairings) input.

## 291 paired-reads (in 16 unique pairings) successfully merged out of 435 (in 27 pairings) input.

## 579 paired-reads (in 38 unique pairings) successfully merged out of 1416 (in 86 pairings) input.

## 201 paired-reads (in 11 unique pairings) successfully merged out of 417 (in 22 pairings) input.

## 280 paired-reads (in 6 unique pairings) successfully merged out of 296 (in 9 pairings) input.

## 1038 paired-reads (in 49 unique pairings) successfully merged out of 1600 (in 89 pairings) input.

## 257 paired-reads (in 9 unique pairings) successfully merged out of 300 (in 16 pairings) input.

## No paired-reads (in ZERO unique pairings) successfully merged out of 5 pairings) input.

## 0 paired-reads (in 0 unique pairings) successfully merged out of 2 (in 1 pairings) input.

## 49 paired-reads (in 1 unique pairings) successfully merged out of 49 (in 1 pairings) input.

## 121 paired-reads (in 1 unique pairings) successfully merged out of 123 (in 2 pairings) input.

## 65 paired-reads (in 1 unique pairings) successfully merged out of 65 (in 1 pairings) input.

## 15 paired-reads (in 1 unique pairings) successfully merged out of 15 (in 1 pairings) input.

## No paired-reads (in ZERO unique pairings) successfully merged out of 6 pairings) input.

## 119 paired-reads (in 4 unique pairings) successfully merged out of 150 (in 8 pairings) input.

## 180 paired-reads (in 8 unique pairings) successfully merged out of 200 (in 12 pairings) input.

# Evaluate the output 
typeof(merged_ASVs)

## [1] "list"

length(merged_ASVs)

## [1] 32

names(merged_ASVs)

##  [1] "SRR17060816_R1_filtered.fastq.gz" "SRR17060817_R1_filtered.fastq.gz"
##  [3] "SRR17060818_R1_filtered.fastq.gz" "SRR17060819_R1_filtered.fastq.gz"
##  [5] "SRR17060820_R1_filtered.fastq.gz" "SRR17060821_R1_filtered.fastq.gz"
##  [7] "SRR17060822_R1_filtered.fastq.gz" "SRR17060823_R1_filtered.fastq.gz"
##  [9] "SRR17060824_R1_filtered.fastq.gz" "SRR17060825_R1_filtered.fastq.gz"
## [11] "SRR17060826_R1_filtered.fastq.gz" "SRR17060827_R1_filtered.fastq.gz"
## [13] "SRR17060828_R1_filtered.fastq.gz" "SRR17060829_R1_filtered.fastq.gz"
## [15] "SRR17060830_R1_filtered.fastq.gz" "SRR17060831_R1_filtered.fastq.gz"
## [17] "SRR17060832_R1_filtered.fastq.gz" "SRR17060833_R1_filtered.fastq.gz"
## [19] "SRR17060834_R1_filtered.fastq.gz" "SRR17060835_R1_filtered.fastq.gz"
## [21] "SRR17060836_R1_filtered.fastq.gz" "SRR17060837_R1_filtered.fastq.gz"
## [23] "SRR17060838_R1_filtered.fastq.gz" "SRR17060839_R1_filtered.fastq.gz"
## [25] "SRR17060840_R1_filtered.fastq.gz" "SRR17060841_R1_filtered.fastq.gz"
## [27] "SRR17060842_R1_filtered.fastq.gz" "SRR17060843_R1_filtered.fastq.gz"
## [29] "SRR17060844_R1_filtered.fastq.gz" "SRR17060845_R1_filtered.fastq.gz"
## [31] "SRR17060846_R1_filtered.fastq.gz" "SRR17060847_R1_filtered.fastq.gz"

# Inspect the merger data.frame from the 20210602-MA-ABB1P 
#head(merged_ASVs[[3]])

Create Raw ASV Count Table

# Create the ASV Count Table 
raw_ASV_table <- makeSequenceTable(merged_ASVs)

# Write out the file to data/01_DADA2


# Check the type and dimensions of the data
dim(raw_ASV_table)

## [1]  32 170

class(raw_ASV_table)

## [1] "matrix" "array"

typeof(raw_ASV_table)

## [1] "integer"

# Inspect the distribution of sequence lengths of all ASVs in dataset 
table(nchar(getSequences(raw_ASV_table)))

## 
## 230 231 242 252 254 255 256 267 271 287 368 373 388 389 396 397 398 399 400 401 
##  21   4   3   4   7  37   1   1   1   1   2   1   1   1   2   7   3   3  16  16 
## 402 404 405 406 412 417 421 422 423 424 
##   6   2   7   1   1   8   1   9   2   1

# Inspect the distribution of sequence lengths of all ASVs in dataset 
# AFTER TRIM
data.frame(Seq_Length = nchar(getSequences(raw_ASV_table))) %>%
  ggplot(aes(x = Seq_Length )) + 
  geom_histogram() + 
  labs(title = "Raw distribution of ASV length")

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

###################################################
###################################################
# TRIM THE ASVS
# Let's trim the ASVs to only be the right size, which is 249.
# 249 originates from our expected amplicon of 252 - 3bp in the forward read due to low quality.

# We will allow for a few 
raw_ASV_table_trimmed <- raw_ASV_table[,nchar(colnames(raw_ASV_table)) %in% 255]

# Inspect the distribution of sequence lengths of all ASVs in dataset 
table(nchar(getSequences(raw_ASV_table_trimmed)))

## 
## 255 
##  37

# What proportion is left of the sequences? 
sum(raw_ASV_table_trimmed)/sum(raw_ASV_table)

## [1] 0.1675704

# Inspect the distribution of sequence lengths of all ASVs in dataset 
# AFTER TRIM
data.frame(Seq_Length = nchar(getSequences(raw_ASV_table_trimmed))) %>%
  ggplot(aes(x = Seq_Length )) + 
  geom_histogram() + 
  labs(title = "Trimmed distribution of ASV length")

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

# Note the peak at 249 is ABOVE 3000

# Let's zoom in on the plot 
data.frame(Seq_Length = nchar(getSequences(raw_ASV_table_trimmed))) %>%
  ggplot(aes(x = Seq_Length )) + 
  geom_histogram() + 
  labs(title = "Trimmed distribution of ASV length") + 
  scale_y_continuous(limits = c(0, 500))

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Taking into account the lower, zoomed-in plot. Do we want to remove those extra ASVs?

Remove Chimeras

Sometimes chimeras arise in our workflow.

Chimeric sequences are artificial sequences formed by the combination of two or more distinct biological sequences. These chimeric sequences can arise during the polymerase chain reaction (PCR) amplification step of the 16S rRNA gene, where fragments from different templates can be erroneously joined together.

Chimera removal is an essential step in the analysis of 16S sequencing data to improve the accuracy of downstream analyses, such as taxonomic assignment and diversity assessment. It helps to avoid the inclusion of misleading or spurious sequences that could lead to incorrect biological interpretations.

# Remove the chimeras in the raw ASV table
noChimeras_ASV_table <- removeBimeraDenovo(raw_ASV_table_trimmed, 
                                           method="consensus", 
                                           multithread=TRUE, verbose=TRUE)

## Identified 0 bimeras out of 37 input sequences.

# Check the dimensions
dim(noChimeras_ASV_table)

## [1] 32 37

# What proportion is left of the sequences? 
sum(noChimeras_ASV_table)/sum(raw_ASV_table_trimmed)

## [1] 1

sum(noChimeras_ASV_table)/sum(raw_ASV_table)

## [1] 0.1675704

# Plot it 
data.frame(Seq_Length_NoChim = nchar(getSequences(noChimeras_ASV_table))) %>%
  ggplot(aes(x = Seq_Length_NoChim )) + 
  geom_histogram()+ 
  labs(title = "Trimmed + Chimera Removal distribution of ASV length")

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

# Note the difference in the peak at 249, which is now BELOW 3000

Track the read counts

Here, we will look at the number of reads that were lost in the filtering, denoising, merging, and chimera removal.

# A little function to identify number seqs 
getN <- function(x) sum(getUniques(x))

# Make the table to track the seqs 
track <- cbind(filtered_reads, 
               sapply(dada_forward, getN),
               sapply(dada_reverse, getN),
               sapply(merged_ASVs, getN),
               rowSums(noChimeras_ASV_table))

head(track)

##                          reads.in reads.out               
## SRR17060816_trim_1.fq.gz   285558       549 302 420 192  9
## SRR17060817_trim_1.fq.gz   676817       278 175 208 155 66
## SRR17060818_trim_1.fq.gz   591364       423 301 338 270 54
## SRR17060819_trim_1.fq.gz   379452       714 458 519 283 40
## SRR17060820_trim_1.fq.gz   570270       604 366 437 157 43
## SRR17060821_trim_1.fq.gz   556682       555 429 464 223 27

# Update column names to be more informative (most are missing at the moment!)
colnames(track) <- c("input", "filtered", "denoisedF", "denoisedR", "merged", "nochim")
rownames(track) <- samples

# Generate a dataframe to track the reads through our DADA2 pipeline
track_counts_df <- 
  track %>%
  # make it a dataframe
  as.data.frame() %>%
  rownames_to_column(var = "names") %>%
  mutate(perc_reads_retained = 100 * nochim / input)

# Visualize it in table format 
DT::datatable(track_counts_df)

# Plot it!
track_counts_df %>%
  pivot_longer(input:nochim, names_to = "read_type", values_to = "num_reads") %>%
  mutate(read_type = fct_relevel(read_type, 
                                 "input", "filtered", "denoisedF", "denoisedR", "merged", "nochim")) %>%
  ggplot(aes(x = read_type, y = num_reads, fill = read_type)) + 
  geom_line(aes(group = names), color = "grey") + 
  geom_point(shape = 21, size = 3, alpha = 0.8) + 
  scale_fill_brewer(palette = "Spectral") + 
  labs(x = "Filtering Step", y = "Number of Sequences") + 
  theme_bw()

Assign Taxonomy

Here, we will use the silva database version 138!

# The next line took 2 mins to run
taxa_train <- 
  assignTaxonomy(noChimeras_ASV_table, 
                 "/workdir/in_class_data/taxonomy/silva_nr99_v138.1_train_set.fa.gz", 
                 multithread=TRUE)

# the next line took 3 minutes 
taxa_addSpecies <- 
  addSpecies(taxa_train, 
             "/workdir/in_class_data/taxonomy/silva_species_assignment_v138.1.fa.gz")

# Inspect the taxonomy 
taxa_print <- taxa_addSpecies # Removing sequence rownames for display only
rownames(taxa_print) <- NULL
#View(taxa_print)

Prepare the data for export!

1. ASV Table

Below, we will prepare the following:

Two ASV Count tables:
1. With ASV seqs: ASV headers include the entire ASV sequence ~250bps.
2. with ASV names: This includes re-written and shortened headers like ASV_1, ASV_2, etc, which will match the names in our fasta file below.
ASV_fastas: A fasta file that we can use to build a tree for phylogenetic analyses (e.g. phylogenetic alpha diversity metrics or UNIFRAC dissimilarty).

Finalize ASV Count Tables

########### 2. COUNT TABLE ###############
############## Modify the ASV names and then save a fasta file!  ############## 
# Give headers more manageable names
# First pull the ASV sequences
asv_seqs <- colnames(noChimeras_ASV_table)
asv_seqs[1:5]

## [1] "AGCCGCGGTAATACGGAGGGTGCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGTGCGTAGGCGGCCTGTTAAGTTGGATGTGAAAGCCCCGGGCTCAACCTGGGAACGGCATCCAAAACTGAGAGGCTCGAGTGCGGAAGAGGAGTGTGGAATTTCCTGTGTAGCGGTGAAATGCGTAGATATAGGAAGGAACACCAGTGGCGAAGGCGACACTCTGGTCTGACACTGACGCTGAGGTACGAAAGCGTGGGGA"
## [2] "AGCCGCGGTAATACGTAAGGGACGAGCGTTATTCGGAATTACTGGGCGTAAAGGGCGTGTAGGCGGTTAATTAGGCTGAGTGTTAAAGACTAGGGCTCAACTCTAGAAGGGCATTCAGAACCGGTTGACTAGAATCTGGTGGAAGGCAACGGAATTTCCCGTGTAGCGGTGGAATGCATAGATATGGGAAGGAACACCAAAGGCGAAGGCAGTTGTCTATGCTGAGATTGACGCTGAGGCGCGAAAGTGTGGGGA"
## [3] "AGCCGCGGTAATACGTAAGGGACGAGCGTTATTCGGAATTACTGGGCGTAAAGGGCGTGTAGGCGGTTAATTAGGCTGAGTGTTAAAGACTGGGGCTCAACTCCAGAAGGGCATTCAGAACCGGTTGACTAGAATCTGGTGGAAGGCAACGGAATTTCCCGTGTAGCGGTGGAATGCATAGATATGGGAAGGAACACCAAAGGCGAAGGCAGTTGTCTATGCCAAGATTGACGCTGAGGCGCGAAAGTGTGGGGA"
## [4] "AGCCGCGGTAATACGTAAGGGACGAGCGTTATTCGGAATTACTGGGCGTAAAGGGCGTGTAGGCGGTTAATTAGGCTGAGTGTTAAAGACTGGGGCTCAACTTCAGAAGGGCATTCAGAACCGGTTGACTAGAATCTGGTGGAAGGCAACGGAATTTCCCGTGTAGCGGTGGAATGCATAGATATGGGAAGGAACACCAAAGGCGAAGGCAGTTGTCTATGCTAAGATTGACGCTGAGGCGCGAAAGTGTGGGGA"
## [5] "AGCCGCGGTAATACGTAAGGGACGAGCGTTATTCGGAATTACTGGGCGTAAAGGGCGTGTAGGCGGTTAATTAGGCTGAGTGTTAAAGACTAGGGCTCAACTCTAGAAGGGCATTCAGAACCGGTTGACTAGAATCTGGTGGAAGGCAACGGAATTTCCCGTGTAGCGGTGGAATGCATAGATATGGGAAGGAACACCAAAGGCGAAGGCAGTTGTCTATGCTAAGATTGACGCTGAGGCGCGAAAGTGTGGGGA"

# make headers for our ASV seq fasta file, which will be our asv names
asv_headers <- vector(dim(noChimeras_ASV_table)[2], mode = "character")
asv_headers[1:5]

## [1] "" "" "" "" ""

# loop through vector and fill it in with ASV names 
for (i in 1:dim(noChimeras_ASV_table)[2]) {
  asv_headers[i] <- paste(">ASV", i, sep = "_")
}

# intitution check
asv_headers[1:5]

## [1] ">ASV_1" ">ASV_2" ">ASV_3" ">ASV_4" ">ASV_5"

##### Rename ASVs in table then write out our ASV fasta file! 
#View(noChimeras_ASV_table)
asv_tab <- t(noChimeras_ASV_table)
#View(asv_tab)

## Rename our asvs! 
row.names(asv_tab) <- sub(">", "", asv_headers)
#View(asv_tab)

2. Taxonomy Table

# Inspect the taxonomy table
#View(taxa_addSpecies)

##### Prepare tax table 
# Add the ASV sequences from the rownames to a column 
new_tax_tab <- 
  taxa_addSpecies%>%
  as.data.frame() %>%
  rownames_to_column(var = "ASVseqs") 
head(new_tax_tab)

##                                                                                                                                                                                                                                                           ASVseqs
## 1 AGCCGCGGTAATACGGAGGGTGCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGTGCGTAGGCGGCCTGTTAAGTTGGATGTGAAAGCCCCGGGCTCAACCTGGGAACGGCATCCAAAACTGAGAGGCTCGAGTGCGGAAGAGGAGTGTGGAATTTCCTGTGTAGCGGTGAAATGCGTAGATATAGGAAGGAACACCAGTGGCGAAGGCGACACTCTGGTCTGACACTGACGCTGAGGTACGAAAGCGTGGGGA
## 2 AGCCGCGGTAATACGTAAGGGACGAGCGTTATTCGGAATTACTGGGCGTAAAGGGCGTGTAGGCGGTTAATTAGGCTGAGTGTTAAAGACTAGGGCTCAACTCTAGAAGGGCATTCAGAACCGGTTGACTAGAATCTGGTGGAAGGCAACGGAATTTCCCGTGTAGCGGTGGAATGCATAGATATGGGAAGGAACACCAAAGGCGAAGGCAGTTGTCTATGCTGAGATTGACGCTGAGGCGCGAAAGTGTGGGGA
## 3 AGCCGCGGTAATACGTAAGGGACGAGCGTTATTCGGAATTACTGGGCGTAAAGGGCGTGTAGGCGGTTAATTAGGCTGAGTGTTAAAGACTGGGGCTCAACTCCAGAAGGGCATTCAGAACCGGTTGACTAGAATCTGGTGGAAGGCAACGGAATTTCCCGTGTAGCGGTGGAATGCATAGATATGGGAAGGAACACCAAAGGCGAAGGCAGTTGTCTATGCCAAGATTGACGCTGAGGCGCGAAAGTGTGGGGA
## 4 AGCCGCGGTAATACGTAAGGGACGAGCGTTATTCGGAATTACTGGGCGTAAAGGGCGTGTAGGCGGTTAATTAGGCTGAGTGTTAAAGACTGGGGCTCAACTTCAGAAGGGCATTCAGAACCGGTTGACTAGAATCTGGTGGAAGGCAACGGAATTTCCCGTGTAGCGGTGGAATGCATAGATATGGGAAGGAACACCAAAGGCGAAGGCAGTTGTCTATGCTAAGATTGACGCTGAGGCGCGAAAGTGTGGGGA
## 5 AGCCGCGGTAATACGTAAGGGACGAGCGTTATTCGGAATTACTGGGCGTAAAGGGCGTGTAGGCGGTTAATTAGGCTGAGTGTTAAAGACTAGGGCTCAACTCTAGAAGGGCATTCAGAACCGGTTGACTAGAATCTGGTGGAAGGCAACGGAATTTCCCGTGTAGCGGTGGAATGCATAGATATGGGAAGGAACACCAAAGGCGAAGGCAGTTGTCTATGCTAAGATTGACGCTGAGGCGCGAAAGTGTGGGGA
## 6 AGCCGCGGTAATACGGAGGATTCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCGGTTTGATAAGTTAGAGGTGAAATGTCGGGGCTCAACCCCGAAACTGCCTCTAATACTGTCAGACTAGAGAGTAGTTGCTGTGGGCGGAATGTATGGTGTAGCGGTGAAATGCTTAGATATCATACAGAACACCGATTGCGAAGGCAGCTCACAAAACTATATCTGACGTTGAGGCACGAAAGCGTGGGGA
##    Kingdom         Phylum               Class           Order
## 1 Bacteria Proteobacteria Gammaproteobacteria Pseudomonadales
## 2 Bacteria  Spirochaetota        Brevinematia  Brevinematales
## 3 Bacteria  Spirochaetota        Brevinematia  Brevinematales
## 4 Bacteria  Spirochaetota        Brevinematia  Brevinematales
## 5 Bacteria  Spirochaetota        Brevinematia  Brevinematales
## 6 Bacteria   Bacteroidota         Bacteroidia   Bacteroidales
##                Family          Genus Species
## 1 Endozoicomonadaceae Endozoicomonas    <NA>
## 2     Brevinemataceae      Brevinema    <NA>
## 3     Brevinemataceae      Brevinema    <NA>
## 4     Brevinemataceae      Brevinema    <NA>
## 5     Brevinemataceae      Brevinema    <NA>
## 6       Rikenellaceae      Alistipes    <NA>

# intution check 
stopifnot(new_tax_tab$ASVseqs == colnames(noChimeras_ASV_table))

# Now let's add the ASV names 
rownames(new_tax_tab) <- rownames(asv_tab)
head(new_tax_tab)

##                                                                                                                                                                                                                                                               ASVseqs
## ASV_1 AGCCGCGGTAATACGGAGGGTGCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGTGCGTAGGCGGCCTGTTAAGTTGGATGTGAAAGCCCCGGGCTCAACCTGGGAACGGCATCCAAAACTGAGAGGCTCGAGTGCGGAAGAGGAGTGTGGAATTTCCTGTGTAGCGGTGAAATGCGTAGATATAGGAAGGAACACCAGTGGCGAAGGCGACACTCTGGTCTGACACTGACGCTGAGGTACGAAAGCGTGGGGA
## ASV_2 AGCCGCGGTAATACGTAAGGGACGAGCGTTATTCGGAATTACTGGGCGTAAAGGGCGTGTAGGCGGTTAATTAGGCTGAGTGTTAAAGACTAGGGCTCAACTCTAGAAGGGCATTCAGAACCGGTTGACTAGAATCTGGTGGAAGGCAACGGAATTTCCCGTGTAGCGGTGGAATGCATAGATATGGGAAGGAACACCAAAGGCGAAGGCAGTTGTCTATGCTGAGATTGACGCTGAGGCGCGAAAGTGTGGGGA
## ASV_3 AGCCGCGGTAATACGTAAGGGACGAGCGTTATTCGGAATTACTGGGCGTAAAGGGCGTGTAGGCGGTTAATTAGGCTGAGTGTTAAAGACTGGGGCTCAACTCCAGAAGGGCATTCAGAACCGGTTGACTAGAATCTGGTGGAAGGCAACGGAATTTCCCGTGTAGCGGTGGAATGCATAGATATGGGAAGGAACACCAAAGGCGAAGGCAGTTGTCTATGCCAAGATTGACGCTGAGGCGCGAAAGTGTGGGGA
## ASV_4 AGCCGCGGTAATACGTAAGGGACGAGCGTTATTCGGAATTACTGGGCGTAAAGGGCGTGTAGGCGGTTAATTAGGCTGAGTGTTAAAGACTGGGGCTCAACTTCAGAAGGGCATTCAGAACCGGTTGACTAGAATCTGGTGGAAGGCAACGGAATTTCCCGTGTAGCGGTGGAATGCATAGATATGGGAAGGAACACCAAAGGCGAAGGCAGTTGTCTATGCTAAGATTGACGCTGAGGCGCGAAAGTGTGGGGA
## ASV_5 AGCCGCGGTAATACGTAAGGGACGAGCGTTATTCGGAATTACTGGGCGTAAAGGGCGTGTAGGCGGTTAATTAGGCTGAGTGTTAAAGACTAGGGCTCAACTCTAGAAGGGCATTCAGAACCGGTTGACTAGAATCTGGTGGAAGGCAACGGAATTTCCCGTGTAGCGGTGGAATGCATAGATATGGGAAGGAACACCAAAGGCGAAGGCAGTTGTCTATGCTAAGATTGACGCTGAGGCGCGAAAGTGTGGGGA
## ASV_6 AGCCGCGGTAATACGGAGGATTCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCGGTTTGATAAGTTAGAGGTGAAATGTCGGGGCTCAACCCCGAAACTGCCTCTAATACTGTCAGACTAGAGAGTAGTTGCTGTGGGCGGAATGTATGGTGTAGCGGTGAAATGCTTAGATATCATACAGAACACCGATTGCGAAGGCAGCTCACAAAACTATATCTGACGTTGAGGCACGAAAGCGTGGGGA
##        Kingdom         Phylum               Class           Order
## ASV_1 Bacteria Proteobacteria Gammaproteobacteria Pseudomonadales
## ASV_2 Bacteria  Spirochaetota        Brevinematia  Brevinematales
## ASV_3 Bacteria  Spirochaetota        Brevinematia  Brevinematales
## ASV_4 Bacteria  Spirochaetota        Brevinematia  Brevinematales
## ASV_5 Bacteria  Spirochaetota        Brevinematia  Brevinematales
## ASV_6 Bacteria   Bacteroidota         Bacteroidia   Bacteroidales
##                    Family          Genus Species
## ASV_1 Endozoicomonadaceae Endozoicomonas    <NA>
## ASV_2     Brevinemataceae      Brevinema    <NA>
## ASV_3     Brevinemataceae      Brevinema    <NA>
## ASV_4     Brevinemataceae      Brevinema    <NA>
## ASV_5     Brevinemataceae      Brevinema    <NA>
## ASV_6       Rikenellaceae      Alistipes    <NA>

### Final prep of tax table. Add new column with ASV names 
asv_tax <- 
  new_tax_tab %>%
  # add rownames from count table for phyloseq handoff
  mutate(ASV = rownames(asv_tab)) %>%
  # Resort the columns with select
  dplyr::select(Kingdom, Phylum, Class, Order, Family, Genus, Species, ASV, ASVseqs)

head(asv_tax)

##        Kingdom         Phylum               Class           Order
## ASV_1 Bacteria Proteobacteria Gammaproteobacteria Pseudomonadales
## ASV_2 Bacteria  Spirochaetota        Brevinematia  Brevinematales
## ASV_3 Bacteria  Spirochaetota        Brevinematia  Brevinematales
## ASV_4 Bacteria  Spirochaetota        Brevinematia  Brevinematales
## ASV_5 Bacteria  Spirochaetota        Brevinematia  Brevinematales
## ASV_6 Bacteria   Bacteroidota         Bacteroidia   Bacteroidales
##                    Family          Genus Species   ASV
## ASV_1 Endozoicomonadaceae Endozoicomonas    <NA> ASV_1
## ASV_2     Brevinemataceae      Brevinema    <NA> ASV_2
## ASV_3     Brevinemataceae      Brevinema    <NA> ASV_3
## ASV_4     Brevinemataceae      Brevinema    <NA> ASV_4
## ASV_5     Brevinemataceae      Brevinema    <NA> ASV_5
## ASV_6       Rikenellaceae      Alistipes    <NA> ASV_6
##                                                                                                                                                                                                                                                               ASVseqs
## ASV_1 AGCCGCGGTAATACGGAGGGTGCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGTGCGTAGGCGGCCTGTTAAGTTGGATGTGAAAGCCCCGGGCTCAACCTGGGAACGGCATCCAAAACTGAGAGGCTCGAGTGCGGAAGAGGAGTGTGGAATTTCCTGTGTAGCGGTGAAATGCGTAGATATAGGAAGGAACACCAGTGGCGAAGGCGACACTCTGGTCTGACACTGACGCTGAGGTACGAAAGCGTGGGGA
## ASV_2 AGCCGCGGTAATACGTAAGGGACGAGCGTTATTCGGAATTACTGGGCGTAAAGGGCGTGTAGGCGGTTAATTAGGCTGAGTGTTAAAGACTAGGGCTCAACTCTAGAAGGGCATTCAGAACCGGTTGACTAGAATCTGGTGGAAGGCAACGGAATTTCCCGTGTAGCGGTGGAATGCATAGATATGGGAAGGAACACCAAAGGCGAAGGCAGTTGTCTATGCTGAGATTGACGCTGAGGCGCGAAAGTGTGGGGA
## ASV_3 AGCCGCGGTAATACGTAAGGGACGAGCGTTATTCGGAATTACTGGGCGTAAAGGGCGTGTAGGCGGTTAATTAGGCTGAGTGTTAAAGACTGGGGCTCAACTCCAGAAGGGCATTCAGAACCGGTTGACTAGAATCTGGTGGAAGGCAACGGAATTTCCCGTGTAGCGGTGGAATGCATAGATATGGGAAGGAACACCAAAGGCGAAGGCAGTTGTCTATGCCAAGATTGACGCTGAGGCGCGAAAGTGTGGGGA
## ASV_4 AGCCGCGGTAATACGTAAGGGACGAGCGTTATTCGGAATTACTGGGCGTAAAGGGCGTGTAGGCGGTTAATTAGGCTGAGTGTTAAAGACTGGGGCTCAACTTCAGAAGGGCATTCAGAACCGGTTGACTAGAATCTGGTGGAAGGCAACGGAATTTCCCGTGTAGCGGTGGAATGCATAGATATGGGAAGGAACACCAAAGGCGAAGGCAGTTGTCTATGCTAAGATTGACGCTGAGGCGCGAAAGTGTGGGGA
## ASV_5 AGCCGCGGTAATACGTAAGGGACGAGCGTTATTCGGAATTACTGGGCGTAAAGGGCGTGTAGGCGGTTAATTAGGCTGAGTGTTAAAGACTAGGGCTCAACTCTAGAAGGGCATTCAGAACCGGTTGACTAGAATCTGGTGGAAGGCAACGGAATTTCCCGTGTAGCGGTGGAATGCATAGATATGGGAAGGAACACCAAAGGCGAAGGCAGTTGTCTATGCTAAGATTGACGCTGAGGCGCGAAAGTGTGGGGA
## ASV_6 AGCCGCGGTAATACGGAGGATTCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCGGTTTGATAAGTTAGAGGTGAAATGTCGGGGCTCAACCCCGAAACTGCCTCTAATACTGTCAGACTAGAGAGTAGTTGCTGTGGGCGGAATGTATGGTGTAGCGGTGAAATGCTTAGATATCATACAGAACACCGATTGCGAAGGCAGCTCACAAAACTATATCTGACGTTGAGGCACGAAAGCGTGGGGA

# Intution check
stopifnot(asv_tax$ASV == rownames(asv_tax), rownames(asv_tax) == rownames(asv_tab))

Write `01_DADA2` files

Now, we will write the files! We will write the following to the data/01_DADA2/ folder. We will save both as files that could be submitted as supplements AND as .RData objects for easy loading into the next steps into R.:

ASV_counts.tsv: ASV count table that has ASV names that are re-written and shortened headers like ASV_1, ASV_2, etc, which will match the names in our fasta file below. This will also be saved as data/01_DADA2/ASV_counts.RData.
ASV_counts_withSeqNames.tsv: This is generated with the data object in this file known as noChimeras_ASV_table. ASV headers include the entire ASV sequence ~250bps. In addition, we will save this as a .RData object as data/01_DADA2/noChimeras_ASV_table.RData as we will use this data in analysis/02_Taxonomic_Assignment.Rmd to assign the taxonomy from the sequence headers.
ASVs.fasta: A fasta file output of the ASV names from ASV_counts.tsv and the sequences from the ASVs in ASV_counts_withSeqNames.tsv. A fasta file that we can use to build a tree for phylogenetic analyses (e.g. phylogenetic alpha diversity metrics or UNIFRAC dissimilarty).
We will also make a copy of ASVs.fasta in data/02_TaxAss_FreshTrain/ to be used for the taxonomy classification in the next step in the workflow.
Write out the taxonomy table
track_read_counts.RData: To track how many reads we lost throughout our workflow that could be used and plotted later. We will add this to the metadata in analysis/02_Taxonomic_Assignment.Rmd.

# FIRST, we will save our output as regular files, which will be useful later on. 
# Save to regular .tsv file 
# Write BOTH the modified and unmodified ASV tables to a file!
# Write count table with ASV numbered names (e.g. ASV_1, ASV_2, etc)
write.table(asv_tab, "data/01_DADA2/ASV_counts.tsv", sep = "\t", quote = FALSE, col.names = NA)
# Write count table with ASV sequence names
write.table(noChimeras_ASV_table, "data/01_DADA2/ASV_counts_withSeqNames.tsv", sep = "\t", quote = FALSE, col.names = NA)
# Write out the fasta file for reference later on for what seq matches what ASV
asv_fasta <- c(rbind(asv_headers, asv_seqs))
# Save to a file!
write(asv_fasta, "data/01_DADA2/ASVs.fasta")


# SECOND, let's save the taxonomy tables 
# Write the table 
write.table(asv_tax, "data/01_DADA2/ASV_taxonomy.tsv", sep = "\t", quote = FALSE, col.names = NA)


# THIRD, let's save to a RData object 
# Each of these files will be used in the analysis/02_Taxonomic_Assignment
# RData objects are for easy loading :) 
save(noChimeras_ASV_table, file = "data/01_DADA2/noChimeras_ASV_table.RData")
save(asv_tab, file = "data/01_DADA2/ASV_counts.RData")
# And save the track_counts_df a R object, which we will merge with metadata information in the next step of the analysis in nalysis/02_Taxonomic_Assignment. 
save(track_counts_df, file = "data/01_DADA2/track_read_counts.RData")

##Session information

#Ensure reproducibility
devtools::session_info()

## ─ Session info ───────────────────────────────────────────────────────────────
##  setting  value
##  version  R version 4.3.2 (2023-10-31)
##  os       Rocky Linux 9.0 (Blue Onyx)
##  system   x86_64, linux-gnu
##  ui       X11
##  language (EN)
##  collate  en_US.UTF-8
##  ctype    en_US.UTF-8
##  tz       America/New_York
##  date     2024-04-15
##  pandoc   3.1.1 @ /usr/lib/rstudio-server/bin/quarto/bin/tools/ (via rmarkdown)
## 
## ─ Packages ───────────────────────────────────────────────────────────────────
##  package              * version    date (UTC) lib source
##  abind                  1.4-5      2016-07-21 [2] CRAN (R 4.3.2)
##  ade4                   1.7-22     2023-02-06 [1] CRAN (R 4.3.2)
##  ape                    5.8        2024-04-11 [1] CRAN (R 4.3.2)
##  Biobase                2.62.0     2023-10-24 [2] Bioconductor
##  BiocGenerics           0.48.1     2023-11-01 [2] Bioconductor
##  BiocParallel           1.36.0     2023-10-24 [2] Bioconductor
##  biomformat             1.30.0     2023-10-24 [1] Bioconductor
##  Biostrings             2.70.1     2023-10-25 [2] Bioconductor
##  bitops                 1.0-7      2021-04-24 [2] CRAN (R 4.3.2)
##  bslib                  0.5.1      2023-08-11 [2] CRAN (R 4.3.2)
##  cachem                 1.0.8      2023-05-01 [2] CRAN (R 4.3.2)
##  callr                  3.7.3      2022-11-02 [2] CRAN (R 4.3.2)
##  cli                    3.6.1      2023-03-23 [2] CRAN (R 4.3.2)
##  cluster                2.1.4      2022-08-22 [2] CRAN (R 4.3.2)
##  codetools              0.2-19     2023-02-01 [2] CRAN (R 4.3.2)
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## ──────────────────────────────────────────────────────────────────────────────

Infer ASVs with DADA2

Calla Bush St George

2024-04-15

Before you start

Set my seed

Goals for this file

Output that we need:

Load Libraries

Load Data

Raw Quality plots

Aggregated Raw Quality Plots

Prepare a placeholder for filtered reads

Filter and Trim Reads

Trimmed Quality Plots

Aggregated Trimmed Plots

Stats on read output from `filterAndTrim`

Error Modeling

Infer ASVs

Merge Forward and Reverse ASVs

Create Raw ASV Count Table

Remove Chimeras

Track the read counts

Assign Taxonomy

Prepare the data for export!

1. ASV Table

Finalize ASV Count Tables

2. Taxonomy Table

Write `01_DADA2` files

Infer ASVs with DADA2

Calla Bush St George

2024-04-15

Before you start

Set my seed

Goals for this file

Output that we need:

Load Libraries

Load Data

Raw Quality plots

Aggregated Raw Quality Plots

Prepare a placeholder for filtered reads

Filter and Trim Reads

Trimmed Quality Plots

Aggregated Trimmed Plots

Stats on read output from filterAndTrim

Error Modeling

Infer ASVs

Merge Forward and Reverse ASVs

Create Raw ASV Count Table

Remove Chimeras

Track the read counts

Assign Taxonomy

Prepare the data for export!

1. ASV Table

Finalize ASV Count Tables

2. Taxonomy Table

Write 01_DADA2 files

Stats on read output from `filterAndTrim`

Write `01_DADA2` files